• …machine learning…

• …Big Data architectures…

• …formal statistical inference…

Image from http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

Statistics courses often focus on the Model-step, this course aims to focus on the other.

Image from http://r4ds.had.co.nz/introduction.html

• As a statistician/actuary/mathematician you will be writing lots of reports.
• It is tempting to create tables and figures by copy, paste, click in and between Excel sheets.
• Exemple: Give me a figure of montly consumer price index 2017
  https://www.google.se/search?q=kpi+scb

• "Intresting decline at the end, but what month is 13?"
• "Nice, but I'd rather have a table?"
• Next year a colleague (or yourself) asks how you did it.

Reproducibility is the ability to get the same research results or inferences, based on the raw data and computer programs provided by researchers.

Cf being able to reproduce the result in an independent experiment (replicability).

• Everything written in code (no copy-paste of values/tables/figures)

• Portable (the code should execute, not only on your computer today)

• Accessible (available openly)

• Fully automated from raw-data to report

summary(mtcars$mpg)
summary(mtcars$"mpg")
summary(mtcars[, "mpg"])
summary(mtcars["mpg"])
summary(mtcars[["mpg"]])
summary(mtcars[1])
summary(mtcars[, 1])
summary(mtcars[[1]])
with(mtcars, summary(mpg))
attach(mtcars); summary(mpg)
summary(subset(mtcars, select=mpg))

From http://r4stats.com/articles/why-r-is-hard-to-learn/

A suite of R-packages heavily influenced by Hadley Wickham at RStudio. Focus in this course.

We need to be able to automatically kombine text, results, tables and figures.

Image from https://rosannavanhespenresearch.files.wordpress.com/

A markup language for typesetting web-pages.

http://writeme.mattstow.com/

Adds executable code to Markdown.

A software for version control and a web-based hosting service.

• A hosting service for collaborative file sharing, mainly code
• Based on Git for version control
• https://github.com/tidyverse/dplyr

Also .Rproj for increased portability.

• Everything written in code: R

• Portable: .Rproj (RStudio)

• Accessible: GitHub

• Automated: R Markdown

Give basic training and preparation for class activities. Not part of examination.

• Preparation: Excersises at DataCamp before class.
• In class: Mainly short reviews/lectures followed by supervised programming excersises.
• Examination:
  • 6 homework problems.
  • Digital exam.
  • Individual project.

• Six sets of problems with deadlines the coming six Sundays.
• To be solved individually.
• Suspicions of plagiarism (e.g. copying of other student's code) will be reported.
• Peer review.
• Missed deadline/fail: Re-examination in February.
• Individual homeworks are not valid for the next course offering (autumn 2019).

• Problem solving in an RStudio environment.
• Tools allowed: Relevant Cheatsheets from RStudio.

• A data-blog-post.
• Illustrate an issue based on an unique data set.
• Short (5 min) oral presentation.

19/11: Sebastian Tengborg, Data scientist at